Effect of magnetic fields on out-of-plane orientations of nematic liquid crystalline polymers under simple shear flow

The effect of magnetic fields on out-of-plane orientations of liquid crystalline polymers (LCPs) under simple shear flows is numerically analyzed using the Doi–Hess equation. The evolution equation for the probability distribution function of the LCP molecules is directly solved without any approximation closure. The initial director is parallel to the vorticity direction. Two cases of the magnetic fields are considered (1) the magnetic field parallel to the flow direction, and (2) the magnetic field parallel to the velocity gradient direction. For both cases a log-rolling orientation state is detected at low shear rates. However, the director is quickly aligned along the direction of magnetic fields because of the deformation of molecules. The field affects on the scalar order parameter rather than the major orientation direction for the magnetic field parallel to the flow direction. On the other hand regarding the magnetic field along the vorticity gradient direction, the effect of the magnetic field is more remarkable on the major orientation in comparison with the effect on the scalar order parameter. Also it is be found that the order parameter is increased obviously with increasing the magnetic fields. It is an efficient way to improve the performance of LCP materials.     

Kinetic study of the thermoluminescence glow curve of natural dolerite

T _m–T _stop and glow curve deconvolution methods have been used to determine the number of glow peaks and kinetic parameters (activation energy E and frequency factor s) associated with the glow peaks in a natural dolerite. The T _m–T _stop method indicated that the glow curve of the mineral is the superposition of at least seven first-order components, whereas deconvolution analysis indicated the presence of at least eight peaks. A possible reason for this discrepancy is given. The kinetic parameters of the eight peaks are presented and used to estimate the lifetimes of the glow peaks. The lifetimes of the peaks at 120.8 and 143 °C are few days. For application in dosimetry and dating, we suggest the use of a preheat temperature around 170 °C to ensure the complete removal of these peaks with small lifetimes.     

Searching and exploring properties of geometric configurations using dynamic software

The decline in enrolments and interest in advanced mathematics studies is of growing concern internationally. Previous research suggests that a range of factors can influence students' academic decisions. The focus of the paper is on one of these potential sources of influence— students' perceptions of the tertiary mathematics learning environment. Data from two large-scale surveys (N = 1883) and from a smaller number of interviews (N = 71) with students enrolled in tertiary mathematics courses at five Australian universities are presented and discussed. Collectively, the survey results and the interview data reveal considerable variations in the quality of the teaching and student support available in different mathematics departments. Students' comments were constructive and offered valuable ideas for improving the existing situation, retaining current students and attracting others to mathematics.     

Mathematics in chemistry: indeterminate forms and their meaning

The mathematical language and its tools are complementary to the formalism in chemistry, in particular at an advanced level. It is thus crucial, for its understanding, that students acquire a solid knowledge in Calculus and that they know how to apply it. The frequent occurrence of indeterminate forms in multiple areas, particularly in Physical Chemistry, justifies the need to properly understand the limiting process in such cases. This article emphasizes the importance of the L’Hôpital's rule as a practical tool, although often neglected, to obtain the more common indeterminate limits, through the use of some specific examples as the radioactive decay, spectrophotometric error, Planck's radiation law, second-order kinetics, or consecutive reactions.     

Graphical Criticism: Some Historical Notes

This article proposes a bootstrap local bandwidth selector for estimating nonparametric additive models. The selector is derived from a bootstrap approximation of the conditional mean squared error, based on a wild bootstrap resampling scheme applied to the estimated residuals. The selector is computed exactly (without involving Monte Carlo approximations) and in practice can be evaluated for many additive estimation methods, including backfitting (bivariate), marginal integration, and mixed methods. We study the consistency of the bootstrap approximation and also carry out an empirical simulation study to explore the performance of the proposed selector in comparison with others. The graphical tool SiZer Map enables us to make meaningful comparisons between local and global selectors.     

A Confidence Region Approach to Tuning for Variable Selection

We develop an approach to tuning of penalized regression variable selection methods by calculating the sparsest estimator contained in a confidence region of a specified level. Because confidence intervals/regions are generally understood, tuning penalized regression methods in this way is intuitive and more easily understood by scientists and practitioners. More importantly, our work shows that tuning to a fixed confidence level often performs better than tuning via the common methods based on Akaike information criterion (AIC), Bayesian information criterion (BIC), or cross-validation (CV) over a wide range of sample sizes and levels of sparsity. Additionally, we prove that by tuning with a sequence of confidence levels converging to one, asymptotic selection consistency is obtained, and with a simple two-stage procedure, an oracle property is achieved. The confidence-region-based tuning parameter is easily calculated using output from existing penalized regression computer packages. Our work also shows how to map any penalty parameter to a corresponding confidence coefficient. This mapping facilitates comparisons of tuning parameter selection methods such as AIC, BIC, and CV, and reveals that the resulting tuning parameters correspond to confidence levels that are extremely low, and can vary greatly across datasets. Supplemental materials for the article are available online.     

AMlet,RAMlet, and GAMlet: Automatic Nonlinear Fitting of Additive Models,Robust and Generalized,With Wavelets

A simple and yet powerful method is presented to estimate nonlinearly and nonparametrically the components of additive models using wavelets. The estimator enjoys the good statistical and computational properties of the Waveshrink scatterplot smoother and it can be efficiently computed using the block coordinate relaxation optimization technique. A rule for the automatic selection of the smoothing parameters, suitable for data mining of large datasets, is derived. The wavelet-based method is then extended to estimate generalized additive models. A primal-dual log-barrier interior point algorithm is proposed to solve the corresponding convex programming problem. Based on an asymptotic analysis, a rule for selecting the smoothing parameters is derived, enabling the estimator to be fully automated in practice. We illustrate the finite sample property with a Gaussian and a Poisson simulation.     

Far Casting Cross-Validation

Cross-validation has long been used for choosing tuning parameters and other model selection tasks. It generally performs well provided the data are independent, or nearly so. Improvements have been suggested which address ordinary cross-validation’s (OCV) shortcomings in correlated data. Whereas these techniques have merit, they can still lead to poor model selection in correlated data or are not readily generalizable to high-dimensional data.

The proposed solution, far casting cross-validation (FCCV), addresses these problems. FCCV withholds correlated neighbors in every aspect of the cross-validation procedure. The result is a technique that stresses a fitted model’s ability to extrapolate rather than interpolate. This generally leads to better model selection in correlated datasets.

Whereas FCCV is less than optimal in the independence case, our improvement of OCV applies more generally to higher dimensional error processes and to both parametric and nonparametric model selection problems. To facilitate introduction, we consider only one application, namely estimating global bandwidths for curve estimation with local linear regression. We provide theoretical motivation and report some comparative results from a simulation experiment and on a time series of annual global temperature deviations. For such data, FCCV generally has lower average squared error when disturbances are correlated.

Supplementary materials are available online.